Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA.
J Mol Biol. 2018 Jun 8;430(12):1814-1828. doi: 10.1016/j.jmb.2018.04.010. Epub 2018 Apr 14.
Ab initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases, the rank of the top-scoring near-native prediction was improved by at least twofold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.
从头开始的蛋白质-蛋白质对接算法通常依赖于实验数据来识别最可能的复合物结构。我们将蛋白质-蛋白质对接与化学交联随后的质谱实验数据相结合。我们使用在我们的实验中鉴定的交联的蛋白质复合物的全面搜索的蛋白质数据库中的 19 个案例来测试我们的方法。我们将交联作为基于欧几里得距离或空隙体积距离的约束。对于大多数测试案例，与没有交联信息的对接相比，排名最高的接近天然预测的排名至少提高了两倍，并且前 5 个预测的成功率几乎增加了两倍。我们的结果表明在保留正确预测和消除假阳性之间存在微妙的平衡。有几个测试案例有多个具有不同界面的组件，我们提出了一种将交联分配到界面的方法。对于这些情况，利用对称信息进一步提高了复杂结构预测的性能。
